Bibliographic record
Abstract
Abstract Chapter 5 studies in depth the risk-based approach to data protection, including its rationale and its scope. It shows that it is only a partial implementation of meta regulation. Contrary to meta regulation, it refrains from delegating the regulatory function of standard setting to the regulatees. Instead of addressing all of the issues associated with the “diagnosis-prescription”diagnosis-prescription| flaw associated with command and” control (ie the selection of standards that will lead to satisfactory regulatory outcomes, and the adequate implementation/compliance with the latter), it only focuses on the better implementation of the data protection provisions. In any case, it is also predicated upon the responsibilisation, and hence, the risk transformation of data controllers’ activities. Such responsibilisation is to be found in the modern principle of accountability. Beyond the GDPR, many contemporary statutes have adopted a similar risk-based approach (even though not explicitly named as such). These include Canada’s PIPEDAPIPEDA|, Council of Europe Convention 108+Convention 108+|, etc. These various statutes are discussed and contrasted. Key to the discussion are issues such as the safeguards and type of regulatory collaboration these statutes provide for (eg data protection impact assessment), or how the risk management obligations fare in comparison to the ISO 31000 risk management StandardISO:31000 risk management Standard 2009|, which can be considered the canon in this matter. Finally, this chapter also examines a number of policy proposals that featured a different type of risk-based approach. Namely, one that espouses meta regulation’s delegation of the standard setting function to the regulatees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".